DFAN [arXiv]

This repository contains the training and test code for the paper "Dual Feature Augmentation Network for Generalized Zero-shot Learning" accepted to BMVC 2023.

Running Environment

The implementation of DFAN is mainly based on Python 3.7.16 and PyTorch 1.12.1. To install all required dependencies:

$ pip install -r requirements.txt

Training Script

$ python main.py --training --dataset DATA_SET --mat_path MAT_PATH

Preparing Dataset and Model

We provide trained models (Google Drive) on three different datasets: CUB, SUN, AWA2 following the data split of xlsa17 in the CZSL/GZSL setting.

Citation:

If you use DFAN in your research, please use the following BibTeX entry.

@article{xiang2023dual,
  title={Dual Feature Augmentation Network for Generalized Zero-shot Learning},
  author={Xiang, Lei and Zhou, Yuan and Duan, Haoran and Long, Yang},
  journal={arXiv preprint arXiv:2309.13833},
  year={2023}
}

References

Parts of our codes based on:

• FaisalAlamri0/ViT-ZSL

Contact

If you have any questions about codes, please don't hesitate to contact us by xl294487391@gmail.com.